Observing phase transitions in mixed-phase clouds using polarimetric and spectral imagery during (AC)3 and CIRRUS-HL

该项目的目的是开发一种从被动遥感中检测混合相云的相位和微物理特性的方法。特别是，我们打算在水平分辨率优于100米的情况下确定云中的液体和冰的比例，以便研究与云寿命高度相关的小尺度过程。这项提议的两个研究问题是：利用多角度偏振图像和光谱图像相结合的方式观测云热力学相分离的精度是多少？北极地区气团交换，特别是冷空气爆发和暖空气入侵过程中，低层混合云量及其位相的典型时空特征是什么？云顶的云相强烈影响着北极地区云量和云层的寿命。与过冷的液态水滴相比，通过Bergeron-Findeisen过程形成的冰导致了更大的降水冰粒。一方面，在光谱热窗口区域的大部分区域，冰的颗粒尺寸和比液态水更大的发射率增加了云的发射率。同时，冰的沉积耗尽了云中的水，减少了云层的云量和寿命。这些过程的代表是目前对北极气候变化的估计中的核心赤字。尤其是相变的时间尺度在模式中没有得到很好的描述，也没有受到观测的很好的约束。模拟和观测不足的一个原因是涉及的量在垂直和水平方向上的小尺度变化：从液体到冰的相变发生在相当薄的云层中，通常是几个100米几何厚度的云层，而影响相变的上升和下降气流在水平尺度上发生在几个10到几个100米的尺度上。为此，我们建议被动云相检测方法向相混合量化方法发展，将两种互补的遥感方法结合起来：(1)用于各种方法的光谱辐射观测，例如MODIS光学厚度、相位和颗粒大小的反演；(2)角度极化辐射分布的成像，允许利用液态水(例如云彩、后向散射光辉)和冰(例如镜面反射)特有的特征。将描述云甲板中从液体到冰的相变的空间和时间尺度。直到最近，我们的高光谱传感器specMACS扩展了广视场偏振敏感2D相机(specMACS-P)，使这些进步成为可能，该相机在EUREC4A的试飞中首次展示了其功能。新型传感器的水平分辨率优于100米。

Aim of the project is to develop a method to detect phase and microphysical properties for mixed-phase clouds from passive remote sensing. Particularly, we intend to determine liquid and ice fraction of clouds at a horizontal resolution of better than 100 m in order to study small-scale processes which are highly relevant for cloud lifetime. The two research questions of this proposal are: At which accuracy is the observation of the cloud thermodynamic phase partitioning possible, utilizing the combination of multi-angle polarimetric and spectral imagery? What are the typical spatio-temporal characteristics of low-level mixed phased cloud amount and their phase change during Arctic air-mass exchanges, in particular cold-air outbreaks and warm-air intrusions?Cloud phase at cloud top strongly affects cloud cover and longevity of cloud decks in the Arctic. The ice formation via the Bergeron-Findeisen process leads to larger precipitating ice particles compared to supercooled liquid water droplets. On one hand the increased particle size and the larger emissivity of ice compared to liquid water over large parts of the spectral thermal window region increase the emissivity of the cloud. At the same time ice sedimentation depletes cloud water and reduces cloudiness and lifetime of the cloud decks. The representation of these processes is a core deficit in current estimates of arctic climate change. Especially the time-scale of the phase transition is not well represented in models and not well constrained by observations.One reason for the modeling and observational deficiencies is the small-scale variability of the involved quantities both in the vertical and horizontal directions: The phase transition fromliquid to ice takes place in rather thin cloud layers of typically several 100 m geometrical thickness, and up- and downdrafts affecting the phase transition occur at a horizontal scale of a few 10 to a few 100 m.To this end we suggest the advancement of passive cloud phase detection methods towards phase mixture quantification methods, combining two complimentary remote sensing approaches: (1) spectral radiance observations as used in various approaches such as the MODIS optical thickness, phase, and particle size retrievals; (2) imaging of the angular polarized radiance distribution which allows exploiting features specific for liquid water (e.g. cloudbow, backscatter glory) and ice (e.g. specular reflection). Spatial and temporal scales of the phase transition from liquid to ice in the cloud decks will be characterized. Only recently these advancements have become possible by the extension of our hyperspectral sensor specMACS by a wide-field, polarization-sensitive 2D camera (specMACS-P) which first demonstrated its capabilities during the test flights for EUREC4A. The new sensor provides horizontal resolution better than 100 m.